However, it is non-trivial to directly apply MTL on GBDT since most of the existing MTL methods are either feature-based or parameter-based [1]}, [2]} but GBDT does not perform feature extraction and is a non-parametric model.
One existing solution is Multi-Task Boosting (MT-B) [3]}, which simultaneously trains task-specific boosted trees with samples from each task, and task-common boosted trees with samples from all the tasks.
The final prediction of one task is determined by combining the predictions of both task-specific boosted trees and task-common boosted trees.
Although MT-B is easy to train and deploy, one significant drawback of MT-B is its high computational complexity, since independent trees for each task need to be learned besides the global one.
